Abstract
Genetics is responsible for 80% of androgenetic alopecia (AGA) predisposition. Several single nucleotide polymorphisms (SNPs) have been linked to AGA risk and the metabolism of its first-line therapies. Genotypic and allelic frequencies have not been described in Mexican individuals; therefore, the aim of this study was to describe the genetic distribution of SNPs associated with AGA predisposition and drug metabolism. Using Real Time-PCR, we genotyped SNPs rs4827528 (AR), rs7680591 (FGF5), rs1042028, rs1042157, rs788068 and rs6839 (SULT1A1) and rs776746 (CYP3A5) in 125 (controls = 60, cases = 65) male volunteers from Northern and Western Mexico. The SULT1A1 SNPs rs1042028 (C/T) and rs788068 (T/A/C) resulted in a 100% distribution of the ancestral allele C and mutated allele A, respectively; rs1042028 diverges from the previously reported frequency, while the rs788068 ancestral allele was found to be more predominant than the reported frequency. Rs1042028, rs788068 and rs4827528, were not in Hardy-Weinberg (HW) equilibrium; conversely, rs1042157 and rs6839, rs776746, and rs7680591 followed HW principles. A statistically significant difference (P<0.05) was obtained for the rs1042157 allelic frequency between cases and controls in Western Mexico. We reported the genotypic and allelic frequencies of seven polymorphisms in Mexican individuals from Northern and Western Mexico.
Keywords: Androgenetic alopecia, Mexican population, genotyping, genetic distribution, predisposition, drug metabolism
Introduction
Androgenetic alopecia (AGA) is an inheritable trait; it is the most frequent cause of hair loss in males and females [1]. In males, there is a typical pattern of progressive hair loss located in the frontal area and up to the vertex; female pattern hair loss presents with diffuse thinning, but the frontal hairline is preserved [2]. AGA prevalence depends on age and ethnicity, e.g. Caucasian males have a 50% chance of developing AGA by the age of 50, while by the age of 70 there is an 80% chance, however in Asian and African American populations, the probability of developing AGA is lower [3]. AGA severity is determined using the Hamilton-Norwood (HN) Scale for Male Pattern Baldness [2], or the Sinclair Hair Shedding Scale for Female Pattern Hair Loss [4]. Other diagnostic methods include dermatoscopy [5], scalp biopsy and trichogram, some of which are invasive techniques [6].
AGA is multifactorial and it is likely caused by hormonal (5-alpha-dihydrotestorone, DHT, binds to androgen receptors, which causes hair follicle miniaturization and shortening of the anagen phase of the hair cycle), lifestyle (daily habits, hair product usage, diet, etc.) and genetic factors [7]. Genetics is the most important factor, determining 80% of AGA predisposition [8].
AGA predisposition has been associated with numerous genes, including androgen receptor gene (AR), the first one to be associated with AGA, and fibroblast growth factor 5 (FGF5) [7,9-11]. FGF5 has been established as a predisposition gene because of its involvement in anagen phase regulation [9,10]. The sulfotransferase family 1A member 1 (SULT1A1), and cytochrome P450 family 3 subfamily A member 5 (CYP3A5) genes code for proteins that regulate the metabolism of several first-line therapies used in AGA [12-14].
Several AR single nucleotide polymorphisms (SNPs) have been identified, including rs4827528, rs5919393 and rs6152. Rs4827528 (A/G/T/C) has the closest association with AGA (OR = 3.4) [10]. Androgen receptors have a fundamental role in the development of AGA as excessive androgen binding (e.g. testosterone and DHT) causes hair follicle miniaturization. Fibroblast growth factor 5 (FGF5) is an anagen phase inhibitor that promotes the transition from anagen to catagen phase. Furthermore, it regulates cellular proliferation and differentiation. Currently, only one FGF5 polymorphism, rs7680591 (T/A/G), shows a direct correlation with AGA. This marker increases gene expression and results in a shortened anagen phase [9,10].
SULT1A1 codes for a catalytic enzyme responsible for the bioactivation of xenobiotics, endogenous compounds, drugs, hormones and neurotransmitters. Bioactivation is carried out by the addition of a sulfonate group to a hydroxyl or amino group. Minoxidil, one of the first line treatment for AGA, is a SULT1A1 substrate [14]. SNPs like rs1042157 (G/A/C), rs6839 (T/C), rs788068 (T/A/C) and rs1042028 or SULT1A1*2 (C/T) have been associated with decreased enzymatic activity [13,15,16].
CYP3A5 codes for a cytochrome P450 member, which is a monooxygenase that catalyzes a range of reactions involved in drug metabolism (e.g.finasteride), cholesterol, steroid, and other lipid syntheses [17]. SNPs such as rs15524 and rs776746 alter the enzyme function, also, both polymorphisms are related to a higher finasteride concentration, still the rs776746 or CYP3A5*3 (T/C) produces a cryptic splice site and the premature termination of the enzyme, thus the CYP3A5 expression is lost [12]. In this case, genotype C/C increases finasteride concentration, and therefore, it is a 5-alpha-reductase (responsible for the testosterone to DHT conversion) inhibitor.
Currently, there are no genotyping studies in native Mexican population related with AGA (predisposition and drug metabolism). The latter is relevant to prescribe a precise treatment to the AGA patient. So, the aim of this investigation was to evaluate the genetic distribution of AGA related polymorphisms rs4827528 (AR), rs7680591 (FGF5), rs1042028, rs1042157, rs788068 and rs6839 (SULT1A1) and rs776746 (CYP3A5) in male volunteers from Northern and Western Mexico.
Materials and methods
Ethical statement
This protocol has been approved (register number CIDICIS/I-0127/20) by the Ethics, Research and Biosafety Committees of the Research and Development Center in Health Sciences (Centro de Investigación y Desarrollo en Ciencias de la Salud, CIDICS) of the Universidad Autonoma de Nuevo Leon (UANL) in Monterrey, Nuevo Leon, Mexico. In agreement with the pertinent research and health legislation (Reglamento de la Ley General de Salud), second title, chapter 1, article 17, section II, this kind of study is classified with the minimum risk. Therefore, regulatory approval by the Mexican Health Ministry is not required. This protocol has been conducted in agreement with the Good Clinical Practices Guidelines of the International Conference on Harmonization (ICH- E6-R1), the Mexican norms NOM-012-SSA3-2012 and NOM-220-SSA1-2016, and the current research and health legislation and other legal dispositions that applied. The volunteers’ name and data are confidential, and they are kept as professional secret. All volunteers understood, accepted, and provided informed consent. The results, identity and medical records of the volunteers are protected according to the NOM-168-SSA1-1998.
Patients and samples
From June 2021 to September 2021, we recruited 125 male volunteers in Monterrey, Nuevo Leon and Guadalajara, Jalisco, México.
Inclusion criteria
All participants were from 18 to 65 years old. The volunteers were divided in two groups in each region (North and West): 1. Control group (volunteers without AGA) and 2. AGA or cases group (volunteers with AGA between category II and IV of the Hamilton-Norwood Scale) (Hamilton, 1951). Both groups included participants whose parents, at least one paternal grandparent, and at least one maternal grandparent were born in Northern (Chihuahua, Coahuila, Nuevo Leon, Sonora, Tamaulipas, Durango, Baja California, Baja California Sur) or Western (Sinaloa, Nayarit, Aguascalientes, Jalisco, Colima, Michoacán, Guanajuato) Mexico.
Exclusion criteria
There were no female volunteers in this study. All volunteers, controls and cases, were exempt of a clinical diagnosis of alopecia areata, trichotillomania, traction alopecia, scarring alopecia, telogen effluvium, discoid or systemic erythematosus lupus, and thyroid disease present during the study or without control (at least 6 months of treatment with an endocrinologist), malignant neoplasm in the last 5 years (except non melanoma skin cancer). We did not recruit volunteers under hormonal replacement therapy or with any hormonal condition with a direct impact in the hair growth cycle, with a BMI <22 or with ancestors born in any other state than the ones mentioned.
Saliva samples were collected from the volunteers using a non-invasive saliva collection kit, Oragene DNA-OG 500 (DNA Genotek, Canada). DNA was extracted to determine the presence or absence of the mentioned SNPs. The genotyping study length was 11 months, including the recruitment, processing and analysis phases.
Hamilton-norwood evaluation
All patients were diagnosed and assigned to a control or case group by a certified dermatologist using the Hamilton-Norwood scale [2] as follows: Type II: The anterior border of the hairline in the frontoparietal regions has triangular areas of recession, which tend to be symmetrical and extend no farther posteriorly than a point of 3 cm, anterior to a line drawn in a coronal plane between the external auditory meatuses. Hair is also lost, or sparse, along the midfrontal border of the scalp, but the depth of the affected area is much less than in the frontoparietal regions.
Type III: Borderline cases were listed separately as Type III, which also included scalps in which classification is rendered inaccurately due to scars, lateral asymmetry in denudation, unusual types of sparseness and thinning of the hair, and other factors.
Type IV: It represents the minimal hair loss considered sufficient to represent baldness. There are deep frontotemporal recessions, usually symmetrical, and they are either bare or covered very sparsely by hair. These recessions extend farther posteriorly than a point, which lies 3 cm anterior to a coronal line drawn between the external auditory meatuses. If hair is sparse or lacking as a broad band along the entire anterior border of the hairline, it is classified as Type IVA.
rhApm SNP assay genotyping
For the DNA extraction and purification from the saliva samples the PureLink™ Genomic DNA Mini Kit (Thermo Fisher Scientific, USA) was used, following the supplier instructions (total volume after elution was 50 microliters, µL). Quantification was performed using the Qubit dsADN HS (Thermo Fisher Scientific, USA) and Qubit 3.0 Fluorometer (Thermo Fisher Scientific, USA). For each reaction 2 µL of the sample and 198 µL of the solution stock (1 µL Qubit dsDNA HS Reagent + 199 µL Qubit dsDNA HS Buffer) were added.
Genotyping was performed with the rhAmp SNP Assay (Integrated DNA Technologies, IDT, USA) (Table 1) and the Real-time Polymerase Chain Reaction (qPCR) technique in a CFX96 Touch-Real Time PCR Detection System (Bio-Rad Laboratories, USA). As positive controls, gBlocks® Gene Fragments (Integrated DNA Technologies, IDT, USA) were used. Reaction components were stock solution 5.3 µL, rhAmp SNP Assay (20X) 0.05 µL, nuclease-free water 4.2 µL with a final volume of 10 µL. Thermocycling conditions were followed according to manufacturer.
Table 1.
rhAmp SNP Assay design IDs (Integrated DNA Technologies, IDT)
| Gene | SNP | Design ID |
|---|---|---|
| AR | Rs4827528 | Hs.GT.rs4827528.G.1 |
| FGF5 | rs7680591 | Hs.GT.rs7680591.A.1 |
| CYP3A5 | rs776746 | Hs.ADME.rs776746.C.1 |
| SULT1A1 | rs1042028 | CD.GT.CGFP1420.1 |
| rs1042157 | CD.GT.NQFT9529.1 | |
| rs6839 | CD.GT.NTVL6110.1 | |
| rs788068 | CD.GT.VXLQ2918.1 |
SNP: Single Nucleotide Polymorphism.
Statistical analysis
To statistically analyze the study results, Hardy-Weinberg Equilibrium (HW, 1 = p2 + 2pq + q2) and square chi (χ2 = Σ [(O-E) 2/E]) were applied. The HW was used to determine if the populations were in equilibrium (p-value <0.05). The non-parametric square chi analysis was used to analyze the allelic and genotypic frequencies. Results were considered statistically significant with p-value <0.05. IBM SPSS package (v 2.0, IBM USA) was used to perform the analysis.
Results
Patients and samples
This case-control study included 125 volunteers (65 cases and 60 controls). The mean age was 29.87 years for the controls and 34.77 years for the cases, being 18-33 the group age with the highest frequency in the controls (66.7%) and the 31-43 in the cases group (41.6%). According to the HN scale, the 53.5% of volunteers within the cases presented a low moderate AGA (III). Control and cases group age distribution, mean BMI and volunteers per HW scale groups are shown in Table 2.
Table 2.
Control and cases group age distribution, mean BMI and volunteers per HW scale groups
| Region | Control Group | Cases (AGA Group) | ||||
|---|---|---|---|---|---|---|
|
|
|
|||||
| North | West | Total | North | West | Total | |
| n (%) | 30 (50.0%) | 30 (50.0%) | 60 | 32 (49.23%) | 33 (50.77%) | 65 |
| Mean age (years) | 31.5 | 28.23 | 29.87 | 37.81 | 31.72 | 34.77 |
| n age groups (years) | ||||||
| 18-30 | 18 (30.0%) | 22 (36.7%) | 40 (66.7%) | 7 (10.8%) | 18 (27.7%) | 25 (38.5%) |
| 31-43 | 6 (10.0%) | 6 (10.0%) | 12 (20.0%) | 15 (23.1%) | 12 (18.5%) | 27 (41.6%) |
| 44-56 | 5 (8.3%) | 2 (3.3%) | 7 (11.7%) | 9 (13.9%) | 3 (4.6%) | 12 (18.5%) |
| 57-65 | 1 (1.7%) | 0 (0.0%) | 1 (1.7%) | 1 (1.6%) | 0 (0.0%) | 1 (1.6%) |
| Mean BMI | 26.2 | 28.61 | 27.41 | 28.23 | 26.03 | 27.13 |
| n HN scale (%) | ||||||
| II | n/a | n/a | n/a | 5 (27.78%) | 13 (72.22%) | 18 |
| III | n/a | n/a | n/a | 14 (46.67%) | 16 (53.33%) | 30 |
| IV | n/a | n/a | n/a | 13 (76.47%) | 4 (23.53%) | 17 |
AGA: Androgenetic Alopecia; BMI: Body Mass Index; HN: Hamilton-Norwood; n/a: Not Applicable.
Genotypic and allelic frequencies
Considering the whole population (North/West) without separating by region, genotypic frequencies (Gf) were obtained (Table 3) for each SNP. In both groups, the polymorphisms rs1042028 and rs788068 (SULT1A1) got a 100% distribution of genotypes C/C and A/A, respectively. The rs4827528 (AR) presented a Gf of 0.90 for genotype G/G in controls and cases. Furthermore, genotypes G/A of rs1042157 and T/C of rs6839 (SULT1A1) and T/A of rs7680591 (FGF5) were predominant with 0.47, 0.45 and 0.53, respectively for controls and 0.61, 0.46 and 0.44, respectively for cases. Among both groups, SNP rs776746 (CYP3A5) there was a predominance for recessive genotype C/C.
Table 3.
Volunteers’ genotypic frequencies showed by polymorphisms, group and region with their HW equilibrium p-value
| SNP | Group | Region | n (Genotypic frequencies) | HW equilibrium | ||
|---|---|---|---|---|---|---|
|
| ||||||
| AA | Aa | aa | p-value | |||
| rs1042028 | Control | North | 30 (0.50) | 0 (0.0) | 0 (0.0) | n/a |
| West | 30 (0.50) | 0 (0.0) | 0 (0.0) | n/a | ||
| North/West | 60 (1.00) | 0 (0.0) | 0 (0.0) | n/a | ||
| Cases | North | 32 (0.50) | 0 (0.0) | 0 (0.0) | n/a | |
| West | 33 (0.50) | 0 (0.0) | 0 (0.0) | n/a | ||
| North/West | 65 (1.00) | 0 (0.0) | 0 (0.0) | n/a | ||
| rs1042157 | Control | North | 8 (0.13) | 13 (0.22) | 9 (0.15) | 0.468 |
| West | 5 (0.08) | 15 (0.25) | 10 (0.17) | 0.875 | ||
| North/West | 13 (0.21) | 28 (0.47) | 19 (0.32) | 0.657 | ||
| Cases | North | 4 (0.06) | 19 (0.30) | 9 (0.14) | 0.219 | |
| West | 10 (0.15) | 20 (0.31) | 3 (0.04) | 0.121 | ||
| North/West | 14 (0.21) | 39 (0.61) | 12 (0.18) | 0.104 | ||
| rs6839 | Control | North | 6 (0.10) | 18 (0.30) | 6 (0.10) | 0.273 |
| West | 11 (0.18) | 9 (0.15) | 10 (0.17) | 0.028 | ||
| North/West | 17 (0.28) | 27 (0.45) | 16 (0.27) | 0.439 | ||
| Cases | North | 3 (0.04) | 15 (0.23) | 14 (0.22) | 0.721 | |
| West | 11 (0.17) | 15 (0.23) | 7 (0.11) | 0.656 | ||
| North/West | 14 (0.21) | 30 (0.46) | 21 (0.33) | 0.596 | ||
| rs788068 | Control | North | 0 (0.0) | 0 (0.0) | 30 (0.5) | n/a |
| West | 0 (0.0) | 0 (0.0) | 30 (0.5) | n/a | ||
| North/West | 0 (0.0) | 0 (0.0) | 60 (1.00) | n/a | ||
| Cases | North | 0 (0.0) | 0 (0.0) | 32 (0.5) | n/a | |
| West | 0 (0.0) | 0 (0.0) | 33 (0.5) | n/a | ||
| North/West | 0 (0.0) | 0 (0.0) | 65 (1.00) | n/a | ||
| rs776746 | Control | North | 3 (0.05) | 11 (0.18) | 16 (0.27) | 0.594 |
| West | 2 (0.03) | 9 (0.15) | 19 (0.32) | 0.524 | ||
| North/West | 5 (0.08) | 20 (0.33) | 35 (0.59) | 0.389 | ||
| Cases | North | 0 (0.0) | 15 (0.23) | 17 (0.26) | 0.083 | |
| West | 0 (0.0) | 8 (0.12) | 25 (0.39) | 0.428 | ||
| North/West | 0 (0.0) | 23 (0.35) | 42 (0.65) | 0.083 | ||
| rs7680591 | Control | North | 6 (0.10) | 18 (0.30) | 6 (0.10) | 0.273 |
| West | 10 (0.17) | 14 (0.23) | 6 (0.10) | 0.785 | ||
| North/West | 16 (0.27) | 32 (0.53) | 12 (0.20) | 0.580 | ||
| Cases | North | 3 (0.4) | 16 (0.25) | 13 (0.20) | 0.540 | |
| West | 11 (0.17) | 12 (0.19) | 10 (0.15) | 0.118 | ||
| North/West | 14 (0.21) | 28 (0.44) | 23 (0.35) | 0.326 | ||
| rs4827528 | Control | North | 3 (0.05) | 0 (0.0) | 27 (0.45) | <0.001* |
| West | 3 (0.05) | 0 (0.0) | 27 (0.45) | <0.001* | ||
| North/West | 6 (0.10) | 0 (0.0) | 54 (0.90) | <0.001* | ||
| Cases | North | 3 (0.04) | 0 (0.0) | 29 (0.45) | <0.001* | |
| West | 4 (0.06) | 0 (0.0) | 29 (0.45) | <0.001* | ||
| North/West | 7 (0.10) | 0 (0.0) | 58 (0.90) | <0.001* | ||
SNP: Single Nucleotide Polymorphism; HN: Hamilton-Norwood; A: Ancestral allele; a: Mutated allele; n/a: Not Applicable.
Significant result (p-value <0.05).
Allelic frequencies (Af) were calculated for each SNP (Table 4). The ancestral allele for SULT1A1 polymorphisms rs1042028 (C), rs1042157 (G), rs6839 (T) and rs788068 (T) was found in 100% (120/120, Af = 1.00), 45% (54/120, Af = 0.45), 51% (61/120, Af = 0.51) and 0% (0/120, Af = 0.00), respectively in the controls and 100% (130/130, Af = 1.00), 52% (67/130, Af = 0.52), 44% (58/130, Af = 0.44) and 0% (0/130, Af = 0.00) in the cases. In contrast, the mutated allele (T, A, C and A respectively) had a distribution of 0% (0/120, Af = 0.00), 55% (66/120, Af = 0.55), 49% (59/120, Af = 0.49) and 100% (120/120, Af = 1.00) in the controls and 0% (0/130, Af = 0.00), 48% (63/130, Af = 0.48), 55% (72/130, Af = 0.55) and 100% (130/130, Af = 1.00) in the AGA group. Ancestral alleles T, T and A were identified in 25% (30/120, Af = 0.25), 53% (64/120, Af = 0.53) and 10% (12/120, Af = 0.10) of the controls, and 18% (23/130, Af = 0.18), 43% (56/130, Af = 0.43) and 10% (14/130, Af = 0.10) of the cases for rs776746, 7680591 and rs4827528 polymorphisms; while mutated alleles C, A and G were reported respectively in 75.0% (90/120, Af = 0.75), 47% (50/120, Af = 0.47) and 90% (108/120, Af = 0.90) in the controls, and 82% (107/130, Af = 0.82), 57% (74/130, Af = 0.57) and 90% (116/120, Af = 0.90) in the AGA group.
Table 4.
Volunteers’ allelic frequencies showed by polymorphisms, group and region with their p-value
| SNP | Group | Allelic frequencies per region | |||||
|---|---|---|---|---|---|---|---|
|
| |||||||
| North | West | North/West | |||||
|
|
|
|
|||||
| A | a | A | a | A | a | ||
| rs1042028 | Control | 60 (0.50) | 0 (0.0) | 60 (0.50) | 0 (0.0) | 120 (1.00) | 0 (0.0) |
| Cases | 64 (0.49) | 0 (0.0) | 66 (0.51) | 0 (0.0) | 130 (1.0) | 0 (0.0) | |
| p-value | n/a | n/a | n/a | ||||
| rs1042157 | Control | 29 (0.24) | 31 (0.26) | 25 (0.21) | 35 (0.29) | 54 (0.45) | 66 (0.55) |
| Cases | 27 (0.21) | 37 (0.28) | 40 (0.31) | 26 (0.20) | 67 (0.52) | 63 (0.48) | |
| p-value | 0.4919 | 0.0336* | 0.3014 | ||||
| rs6839 | Control | 30 (0.25) | 30 (0.25) | 31 (0.26) | 29 (0.24) | 61 (0.51) | 59 (0.49) |
| Cases | 21 (0.16) | 43 (0.33) | 37 (0.28) | 29 (0.22) | 58 (0.44) | 72 (0.55) | |
| p-value | 0.0519 | 0.6212 | 0.3254 | ||||
| rs788068 | Control | 0 (0.0) | 60 (0.50) | 0 (0.0) | 60 (0.50) | 0 (0.0) | 120 (1.00) |
| Cases | 0 (0.0) | 64 (0.49) | 0 (0.0) | 66 (0.51) | 0 (0.0) | 130 (1.00) | |
| p-value | n/a | n/a | n/a | ||||
| rs776746 | Control | 17 (0.14) | 43 (0.36) | 13 (0.11) | 47 (0.39) | 30 (0.25) | 90 (0.75) |
| Cases | 15 (0.12) | 49 (0.38) | 8 (0.06) | 58 (0.44) | 23 (0.18) | 107 (0.82) | |
| p-value | 0.5335 | 0.1510 | 0.1578 | ||||
| rs7680591 | Control | 30 (0.25) | 30 (0.25) | 34 (0.28) | 26 (0.22) | 64 (0.53) | 56 (0.47) |
| Cases | 22 (0.17) | 42 (0.32) | 34 (0.26) | 32 (0.25) | 56 (0.43) | 74 (0.57) | |
| p-value | 0.0780 | 0.5623 | 0.1049 | ||||
| rs487528 | Control | 6 (0.05) | 54 (0.45) | 6 (0.05) | 54 (0.45) | 12 (0.10) | 108 (0.90) |
| Cases | 6 (0.04) | 58 (0.45) | 8 (0.06) | 58 (0.45) | 14 (0.10) | 116 (0.90) | |
| p-value | 0.9064 | 0.6150 | 0.8422 | ||||
SNP: Single Nucleotide Polymorphism; A: Ancestral allele; a: Mutated allele; n/a: Not Applicable.
Significant result (p-value <0.05).
A statistical analysis was performed to establish if the behavior of the alleles and genotypes complied with the HW equilibrium in the controls. It was observed that rs1042157 (χ2 = 0.196, P = 0.657) and rs6839 (χ2 = 0.597, p-value = 0.439) of SULT1A1, rs776746 (χ2 = 0.740, p-value = 0.389) of CYP3A5 and rs7680591 (χ2 = 0.306, p-value = 0.580) of FGF5, were in HW equilibrium, considering the population as a whole. The rs1042028 and rs788068 of SULT1A1, and rs4827528 (χ2 = 61, p-value <0.001) of AR, did not show any behavior according to what is postulated by the HW law.
Group comparative analysis
Using the χ2 test, a comparative analysis of the genotypic and allelic frequencies of the seven SNPs under evaluation, was performed between the cases and the control groups. The comparison between groups was made in both ways: 1) without considering geographic areas and 2) separating the total population by geographic regions. The comparative analysis when considering the population as one, did not show significant differences between the allelic and genotypic distribution of the cases and the control groups (p-values >0.05). However, when making the comparison considering the regions separately, there was a statistic difference in the West volunteers between the cases and the controls when the allelic frequency was analyzed, corresponding to rs1042157 (SULT1A1) (P<0.05).
Discussion and conclusion
Case-control studies have been used in genetic investigations to evaluate the genetic predisposition to human conditions and identify SNPs that can help to develop better prognosis and treatments [22]. To elucidate the influence of the genetic factor in AGA development, many researchers have done the analysis of genes associated to processes involved in the AGA pathogeny, e.g. AR gene that has been identified to take part in the sexual steroids route [18], besides, evidence of high AR expression levels in AGA patients have been found [19-21]. The current study aimed to report the allelic and genotypic frequencies of seven SNPs possibly associated with AGA development and drug metabolism in Mexican population.
From the seven included SNPs, rs4827528 of AR gene and rs7680591 of FGF5 gene are recognized as AGA predisposition polymorphisms; while rs1042028, rs1042157, rs788068, and rs6839 of SULT1A1 gene, and rs775746 of CYP3A5 gene, may be associated to the drug metabolism used in AGA treatment.
Even though the Af are consistent with the ones reported in LA Mexican residents (California, U.S.A), Latin-American and American population, depending on the case, the SULT1A1 SNPs rs1042028 (C/T) and rs788068 (T/A/C) showed a 100% distribution of the ancestral allele C and mutated allele A, respectively; rs1042028 diverges from the previous reported frequency (100% for the mutated allele) [23], while the rs788068 ancestral allele was found to be more predominant than the reported frequency (45% for allele T) [24]. Neither rs1042028 nor rs788068 SNPs of SULT1A1 gene, plus the AR polymorphism rs4827528, were in HW equilibrium; conversely, rs1042157 and rs6839 of SULT1A1, rs776746 of CYP3A5 and rs7680591 of FGF5 followed the HW principles.
HW equilibrium is used as a quality control to detect genotyping errors in genetic studies, however disequilibrium (HWD) could occur for other reasons like chance, genome location, HW principles failure or the genetics of the condition per se [25]. Besides, the HWD can be expected in case-control studies in specific locus and small samples [26]. We believe that, it is the case of AR SNP (rs4827528), which is located in chromosome X and its distribution is different from autosomal SNPs [27]. While in other studies, the AR SNP has been found to be in HW equilibrium [7,9,10], in this protocol the HWD can be the result of the limited sample size and the SNP location in chromosome X.
Despite the vast SNP diversity analyzed and the limited sample size, we could identify a significant difference (p-value = 0.024) in Af (mutated allele A) of rs1042157 (SULT1A1) between cases and controls in the Western region. Nevertheless, interestingly, when we analyzed the full population (North and West together) and Northern region alone, we could not identify that difference. It may be explained by the population’s racial composition in different geographic regions of Mexico.
Racial ancestry studies that have been conducted in Mexico tend to cluster the Northern and Western regions as one due to the similar ancestry proportions (~60-80% European, ~21-25% Amerindian, ~13-20% Eurasian and ~1-9% African) [28-30]. All studies concluded that, European ancestry is predominant in Northern and Western population, but there are significant differences in Amerindian and African ancestry percentages, e.g. 5% and 12% Amerindian ancestry for Northern and Western region, respectively [28].
Different results could be explained by the markers used in the study, their location and sample size. Regarding the markers and their genomic location, mitochondrial, autosomal and Y-linked markers elucidate the admixture bias resulting from sex. In Nuevo Leon state (Northern region), the main Y-chromosome contribution comes predominantly from European males (78%) [31]; in contrast, the maternal counterpart (mitochondrial DNA) had Amerindian predominance in Northern (89.1%) and Western (82%) Mexico [32]. It was previously stated that the paternal and maternal proportions are due to the fact that it was common for male Europeans and female Native Americans to breed, and not otherwise [30]. In addition, the use of autosomal markers produces different ancestry proportions; A study reported 56% Amerindian and 38% European ancestry in Nuevo Leon state [31], similar to the data published in another study for the northern state of Coahuila, where the estimated Amerindian ancestry was 55.6% [33]. Opposite results could be due to the genetic intrapopulation structure, which includes socioeconomic and educational factors as demonstrated in Mexico City population [32] and ethnic groups located in each region, like Chichimecas in the North [34] and Huicholes in the West [29]. Our results, considering the analysis with one chromosome X, and six autosomal markers, and the regions chosen for the study, could be explained by the aforementioned data: the lack of significant differences when analyzing the North population alone, and the significant difference between cases and controls in the Af of Western Mexico.
Even though SULT1A1 is not a gene that relates to biosynthetic routes or response to sexual steroids, the fact that we reported the significantly different polymorphism between cases and controls is relevant because of its participation in the minoxidil conversion to its bioactive form [14]. Certain SNPs could negatively affect the sulfotransferase activity of SULT1A1 enzyme, which could directly affect the AGA treatment response to minoxidil, being its active form conjugated to a sulfate group.
As we have recognized along the manuscript, the main shortcoming of the present study was the sample size. Although this was a pilot study, the number of cases and controls recruited allowed us to perform a full statistical analysis. However, we do not discard to increase sample size avoiding HWD and facilitating the observation of statistical differences which remain as tendences in the present study. For example, AR gene SNP was expected to show differences between study groups and only showed tendences (P = 0.05), although we must take into consideration that most cases were younger than 50 years old, so it’s possible that they could be genetically predisposed to AGA but have not clinically present it yet.
The present study reported the genotypic and allelic frequencies of seven polymorphisms before mentioned in Northern and Western Mexican population, and the possible association for rs1042157 (SULT1A1). Now, it is necessary to increase sample size to investigate the possible association of the SNPs with the genetic AGA predisposition. And to perform a new protocol to study the possible association with the response to treatment using first line therapeutic drugs for AGA (Minoxidil and Finasteride) with the intention of personalizing AGA’s treatment in Mexican population.
Acknowledgements
Special thanks to OmicronLab S.A. de C.V. for their unwavering support and numerous contributions.
Disclosure of conflict of interest
None.
References
- 1.Heilmann-Heimbach S, Hochfeld LM, Paus R, Nöthen MM. Hunting the genes in male-pattern alopecia: how important are they, how close are we and what will they tell us? Exp Dermatol. 2016;25:251–257. doi: 10.1111/exd.12965. [DOI] [PubMed] [Google Scholar]
- 2.Hamilton JB. Patterned loss of hair in man: types and incidence. Ann N Y Acad Sci. 1951;53:708–28. doi: 10.1111/j.1749-6632.1951.tb31971.x. [DOI] [PubMed] [Google Scholar]
- 3.Lolli F, Pallotti F, Rossi A, Fortuna MC, Caro G, Lenzi A, Sansone A, Lombardo F. Androgenetic alopecia: a review. Endocrine. 2017;57:9–17. doi: 10.1007/s12020-017-1280-y. [DOI] [PubMed] [Google Scholar]
- 4.Sinclair R, Jolley D, Mallari R, Magee J. The reliability of horizontally sectioned scalp biopsies in the diagnosis of chronic diffuse telogen hair loss in women. J Am Acad Dermatol. 2004;51:189–199. doi: 10.1016/s0190-9622(03)00045-8. [DOI] [PubMed] [Google Scholar]
- 5.Blume-Peytavi U, Vogt A. Current standards in the diagnostics and therapy of hair diseases-hair consultation. J Dtsch Dermatol Ges. 2011;9:394–410. doi: 10.1111/j.1610-0387.2011.07609.x. quiz 411-2. [DOI] [PubMed] [Google Scholar]
- 6.Vujovic A, Del Marmol V. The female pattern hair loss: review of etiopathogenesis and diagnosis. BioMed Res Int. 2014;2014:767628. doi: 10.1155/2014/767628. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Heilmann-Heimbach S, Herold C, Hochfeld LM, Hillmer AM, Nyholt DR, Hecker J, Javed A, Chew EG, Pechlivanis S, Drichel D, Heng XT, Del Rosario RC, Fier HL, Paus R, Rueedi R, Galesloot TE, Moebus S, Anhalt T, Prabhakar S, Li R, Kanoni S, Papanikolaou G, Kutalik Z, Deloukas P, Philpott MP, Waeber G, Spector TD, Vollenweider P, Kiemeney LA, Dedoussis G, Richards JB, Nothnagel M, Martin NG, Becker T, Hinds DA, Nöthen MM. Meta-analysis identifies novel risk loci and yields systematic insights into the biology of male-pattern baldness. Nat Commun. 2017;8:14694. doi: 10.1038/ncomms14694. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Nyholt DR, Gillespie NA, Heath AC, Martin NG. Genetic basis of male pattern baldness. In J Invest Dermatol. 2003;121:1561–1564. doi: 10.1111/j.1523-1747.2003.12615.x. [DOI] [PubMed] [Google Scholar]
- 9.Hagenaars SP, Hill WD, Harris SE, Ritchie SJ, Davies G, Liewald DC, Gale CR, Porteous DJ, Deary IJ, Marioni RE. Genetic prediction of male pattern baldness. PLoS Genet. 2017;13:e1006594. doi: 10.1371/journal.pgen.1006594. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Pirastu N, Joshi PK, de Vries PS, Cornelis MC, McKeigue PM, Keum N, Franceschini N, Colombo M, Giovannucci EL, Spiliopoulou A, Franke L, North KE, Kraft P, Morrison AC, Esko T, Wilson JF. GWAS for male-pattern baldness identifies 71 susceptibility loci explaining 38% of the risk. Nat Commun. 2017;8:1584. doi: 10.1038/s41467-017-01490-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Yap CX, Sidorenko J, Wu Y, Kemper KE, Yang J, Wray NR, Robinson MR, Visscher PM. Dissection of genetic variation and evidence for pleiotropy in male pattern baldness. Nat Commun. 2018;9:5407. doi: 10.1038/s41467-018-07862-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Chau CH, Price DK, Till C, Goodman PJ, Chen X, Leach RJ, Johnson-Pais TL, Hsing AW, Hoque A, Tangen CM, Chu L, Parnes HL, Schenk JM, Reichardt JK, Thompson IM, Figg WD. Finasteride concentrations and prostate cancer risk: results from the prostate cancer prevention trial. PLoS One. 2015;10:e0126672. doi: 10.1371/journal.pone.0126672. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Daniels J, Kadlubar S. Pharmacogenetics of SULT1A1. Pharmacogenomics. 2014;1514:1823–1838. doi: 10.2217/pgs.14.134. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Gamage N, Barnett A, Hempel N, Duggleby RG, Windmill KF, Martin JL, McManus ME. Human sulfotransferases and their role in chemical metabolism. Toxicol Sci. 2006;90:5–22. doi: 10.1093/toxsci/kfj061. [DOI] [PubMed] [Google Scholar]
- 15.Nagar S, Walther S, Blanchard RL. Sulfotransferase (SULT) 1A1 polymorphic variants *1, *2, and *3 are associated with altered enzymatic activity, cellular phenotype, and protein degradation. Mol Pharmacol. 2006;69:2084–2092. doi: 10.1124/mol.105.019240. [DOI] [PubMed] [Google Scholar]
- 16.Sanchez-Spitman AB, Dezentjé VO, Swen JJ, Moes DJAR, Gelderblom H, Guchelaar HJ. Genetic polymorphisms of 3’-untranslated region of SULT1A1 and their impact on tamoxifen metabolism and efficacy. Breast Cancer Res Treat. 2018;172:401–411. doi: 10.1007/s10549-018-4923-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Kuehl P, Zhang J, Lin Y, Lamba J, Assem M, Schuetz J, Watkins PB, Daly A, Wrighton SA, Hal SD, Maurel P, Relling M, Brimer C, Yasuda K, Venkataramanan R, Strom S, Thummel K, Boguski MS, Schuetz E. Sequence diversity in CYP3A promoters and characterization of the genetic basis of polymorphic CYP3A5 expression. Nat Gen. 2001;27:383–391. doi: 10.1038/86882. [DOI] [PubMed] [Google Scholar]
- 18.Jänne OA, Palvimo JJ, Kallio P, Mehto M. Androgen receptor and mechanism of androgen action. Ann Med. 1993;25:83–89. doi: 10.3109/07853899309147863. [DOI] [PubMed] [Google Scholar]
- 19.Hibberts NA, Howell AE, Randall VA. Balding hair follicle dermal papilla cells contain higher levels of androgen receptors than those from non-balding scalp. J Endocrinol. 1998;156:59–65. doi: 10.1677/joe.0.1560059. [DOI] [PubMed] [Google Scholar]
- 20.Randall VA. Role of 5 alpha-reductase in health and disease. Baillieres Clin Endocrinol Metab. 1994;8:405–431. doi: 10.1016/s0950-351x(05)80259-9. [DOI] [PubMed] [Google Scholar]
- 21.Sawaya ME, Price VH. Different levels of 5α-reductase type I and II, aromatase, and androgen receptor in hair follicles of women and men with androgenetic alopecia. J Invest Dermatol. 1997;109:296–300. doi: 10.1111/1523-1747.ep12335779. [DOI] [PubMed] [Google Scholar]
- 22.Hirschhorn JN, Lohmueller K, Byrne E, Hirschhorn K. A comprehensive review of genetic association studies. Genet Med. 2002;4:45–61. doi: 10.1097/00125817-200203000-00002. [DOI] [PubMed] [Google Scholar]
- 23.Phan L, Jin H, Zhang W, Qiang E, Shekhtman D, Shao D, Revoe R, Villamarin E, Ivanchenko M, Kimura ZY, Wang L, Hao N, Sharopova M, Bihan A, Sturcke M, Lee N, Popova W, Wu C, Bastiani M, Ward JB, Holmes V, Lyoshin K, Kaur EM, B LK. ALFA: allele frequency aggregator. National center for biotechnology information, U.S. National Library of Medicine. 2020 [Google Scholar]
- 24.1000 Genomes Project Consortium. Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, Marchini JL, McCarthy S, McVean GA, Abecasis GR. A global reference for human genetic variation. Nature. 2015;526:68–74. doi: 10.1038/nature15393. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Xu J, Turner A, Little J, Bleecker ER, Meyers DA. Positive results in association studies are associated with departure from Hardy-Weinberg equilibrium: hint for genotyping error? Hum Genet. 2002;111:573–574. doi: 10.1007/s00439-002-0819-y. [DOI] [PubMed] [Google Scholar]
- 26.Wittke-Thompson JK, Pluzhnikov A, Cox NJ. Rational inferences about departures from HWE. Am J Hum Genet. 2005;76:967–986. doi: 10.1086/430507. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Graffelman J, Weir BS. Testing for Hardy-Weinberg equilibrium at biallelic genetic markers on the X chromosome. Heredity (Edinb) 2016;116:558–568. doi: 10.1038/hdy.2016.20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Martínez-Cortés G, Salazar-Flores J, Fernández-Rodríguez LG, Rubi-Castellanos R, Rodríguez-Loya C, Velarde-Félix JS, Muñoz-Valle JF, Parra-Rojas I, Rangel-Villalobos H. Admixture and population structure in Mexican-Mestizos based on paternal lineages. J Hum Genet. 2012;57:568–574. doi: 10.1038/jhg.2012.67. [DOI] [PubMed] [Google Scholar]
- 29.Rangel-Villalobos H, Muñoz-Valle JF, González-Martín A, Gorostiza A, Magaña MT, Páez-Riberos LA. Genetic admixture, relatedness, and structure patterns among Mexican populations revealed by the Y-chromosome. Am J Phys Anthropol. 2008;135:448–461. doi: 10.1002/ajpa.20765. [DOI] [PubMed] [Google Scholar]
- 30.Rubi-Castellanos R, Martínez-Cortés G, Muñoz-Valle JF, González-Martín A, Cerda-Flores RM, Anaya-Palafox M, Rangel-Villalobos H. Pre-hispanic Mesoamerican demography approximates the present-day ancestry of Mestizos throughout the territory of Mexico. Am J Physical Anthropol. 2009;139:284–294. doi: 10.1002/ajpa.20980. [DOI] [PubMed] [Google Scholar]
- 31.Martinez-Fierro ML, Beuten J, Leach RJ, Parra EJ, Cruz-Lopez M, Rangel-Villalobos H, Riego-Ruiz LR, Ortiz-Lopez R, Martinez-Rodriguez HG, Rojas-Martinez A. Ancestry informative markers and admixture proportions in Mexico. J Hum Genet. 2009;54:504–509. doi: 10.1038/jhg.2009.65. [DOI] [PubMed] [Google Scholar]
- 32.Martínez-Mariagnac VL, Valladares A, Cameron E, Chan A, Perera A, Globus-Goldberg R, Wacher N, Kumate J, McKeigue P, O’Donell D, Shriver MD, Cruz M, Parra EJ. Admixture in Mexico city: implications for admixture mapping of type 2 diabetes genetic risk factors. Hum Genet. 2007;120:807–819. doi: 10.1007/s00439-006-0273-3. [DOI] [PubMed] [Google Scholar]
- 33.Lisker R, Ramirez E, Babinsky V. Genetic structure of autochthonous populations of Meso-America: Mexico. Hum Biol. 1996;68:395–404. [PubMed] [Google Scholar]
- 34.Barquera R, Bravo-Acevedo A, Clayton S, Munguía TJR, Hernández-Zaragoza DI, Adalid-Sáinz C, Arrieta-Bolaños E, Aquino-Rubio G, González-Martínez MDR, Lona-Sánchez A, Martínez-Álvarez JC, Arrazola-García MA, Delgado-Aguirre H, González-Medina L, Pacheco-Ubaldo H, Juárez-Barreto V, Benítez-Arvizu G, Escareño-Montiel N, Juárez-de la Cruz F, Jaramillo-Rodríguez Y, Salgado-Adame A, Zúñiga J, Yunis EJ, Bekker-Méndez C, Granados J. Genetic diversity of HLA system in two populations from Nuevo León, Mexico: Monterrey and rural Nuevo León. Hum Immunol. 2020;81:516–518. doi: 10.1016/j.humimm.2019.06.003. [DOI] [PubMed] [Google Scholar]